Landslide hazards affect the security of human life and property. Mapping the spatial distribution of landslide hazard risk is critical for decision-makers to implement disaster prevention measures. This study aimed to predict and zone landslide hazard risk, using Guixi County in eastern Jiangxi, China, as an example. An integrated dataset composed of 21 geo-information layers, including lithology, rainfall, altitude, slope, distances to faults, roads and rivers, and thickness of the weathering crust, was used to achieve the aim. Non-digital layers were digitized and assigned weights based on their landslide propensity. Landslide locations and non-risk zones (flat areas) were both vectorized as polygons and randomly divided into two groups to create a training set (70%) and a validation set (30%). Using this training set, the Random Forests (RF) algorithm, which is known for its accurate prediction, was applied to the integrated dataset for risk modeling. The results were assessed against the validation set. Overall accuracy of 91.23% and Kappa Coefficient of 0.82 were obtained. The calculated probability for each pixel was consequently graded into different zones for risk mapping. Hence, we conclude that landslide risk zoning using the RF algorithm can serve as a pertinent reference for local government in their disaster prevention and early warning measures.

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Abstract-A fundamental issue in all-optical switching is to offer efficient and cost-effective transport services for a wide range of bandwidth granularities. This paper presents multi-granular optical crossconnect (MG-OXC) architectures that combine slow (ms regime) and fast (ns regime) switch elements, in order to support optical circuit switching (OCS), optical burst switching (OBS), and even optical packet switching (OPS). The MG-OXC architectures are designed to provide a cost-effective approach, while offering the flexibility and reconfigurability to deal with dynamic requirements of different applications. All proposed MG-OXC designs are analyzed and compared in terms of dimensionality, flexibilityÕ reconfigurability, and scalability. Furthermore, node level simulations are conducted to evaluate the performance of MG-OXCs under different traffic regimes. Finally, the feasibility of the proposed architectures is demonstrated on an application-aware, multi-bitrate (10 and 40 Gbps), end-to-end OBS testbed.

The paper presents the architecture, implementation and evaluation of the flexible and finely granular Time Shared Optical Network (TSON) metro node. It focuses on the FPGA-based Layer 2 TSON metro node system. The experimentally measured results show exceptional performance of up to 8.68 Gbps throughput per 10 Gbps port, 95.38% of theoretical maximum throughput, latency of less than 160 μsec and jitter of less than 25 μsec. The TSON topology agnostic node/network also delivers differentiated QoS latency levels yet always guaranteed (contention-free) by deploying diverse time-slice allocation schemes.

Landslides constitute a severe environmental problem in Jiangxi, China. This research was aimed at conducting landslide hazard assessment to provide technical support for disaster reduction and prevention action in the province. Fourteen geo-environmental factors, e.g., slope, elevation, road, river, fault, lithology, rainfall, and land cover types, were selected for this study. A test was made in two cases: (1) only based on the main linear features, e.g., main rivers and roads, and (2) with detailed complete linear features including all levels of roads and rivers. After buffering of the linear features, an information value (IV) analysis was applied to quantify the distribution of the observed landslides for each subset of the 14 factors. The results were inputted into the binary logistic regression model (LRM) for landslide risk modeling, taking the known landslide points as a training set (70% of the total 9,525 points). The calculated probability of a landslide was further classified into five grades with an interval of 0.2 for hazard mapping: very high (3.70%), high (4.05%), moderate (18.72%), low (27.17%), and stable zones (46.36%). The accuracy was evaluated by AUC [the area under the receiver operating characteristic (ROC) curve] vs. the validation set (30%, the remaining landslides). The final results show that with increasing the completeness of the linear features, the modeling reliability also significantly increased. We hence concluded that the tested methodology is capable of achieving the landslide hazard prediction at regional scale, and the results may provide technical support for geohazard reduction and prevention in the studied province.